A computer network, such as a local area network (LAN), will have many different intercoupled components. As such, the network will have many areas in which a fault may occur. After a fault occurs, a human diagnostician will normally troubleshoot the LAN by performing tests on the specific components in the LAN based upon his prior experiences of troubleshooting.
Computer networks are becoming increasingly heterogeneous, with different vendors' equipment--running different network protocols--communicating, cooperating and competing to serve a customer's enterprise. Contemporary network troubleshooting methods are not sufficient to manage the complex, multi-vendor networks. To provide the best customer service, new approaches are needed for network fault diagnosis.
Previous artificial intelligence approaches to network diagnosis have been described which use rule-based reasoning, in which rules specify a diagnosis for a system based upon the fulfillment of rule conditions. This approach is very brittle and requires an extremely large set of rules to deal with enough situations to provide a useful diagnostic tool.
An alternative approach to rule-based reasoning is model-based reasoning, an artificial intelligence technology in which structural and functional information about an object, such as a component of a computer network or the network itself, is derived from a model of the object. Model-based reasoning allows a more robust approach to diagnosis. However, model-based reasoning approaches have been criticized as requiring excessively "deep" knowledge of the individual network components. There is a need for a network troubleshooting system using model-based reasoning techniques that can diagnose faults in arbitrarily complex computer networks comprising heterogeneous equipment, while overcoming the requirement of "deep" knowledge. Such a system needs to deduce the nature and location of structural faults, using a model of the network, a model of diagnostic expertise, and behavioral descriptions including reported malfunctions.